MineDojo/Voyager

Features

• Lifelong Learning Models - Provides a lifelong learning framework that continuously discovers and stores new skills as reusable code fragments.
• Lifelong Learning Frameworks - Functions as a lifelong learning framework that continuously discovers new skills and stores them as reusable code fragments.
• Agent Skill Libraries - Maintains a structured library of learned behavioral patterns as code fragments for reuse in complex tasks.
• Autonomous Agents - Integrates LLMs with memory and tool usage to autonomously navigate and interact with virtual environments.
• Automatic Curriculum Generation - Implements an automatic curriculum generator that creates exploration goals to discover new items and behaviors without human guidance.
• Code and UI Generation - Implements the capability to generate and iteratively refine executable code based on real-time environment feedback.
• Code-Generating Agents - Uses a large language model to iteratively write and execute code scripts to control an agent in a virtual world.
• Curriculum Learning Frameworks - Provides a framework for curriculum learning to systematically discover new items and behaviors in virtual environments.
• Embodied AI Exploration - Develops systems that autonomously explore virtual worlds to discover new items and learn complex environmental interactions.
• Skill-Based Lifelong Learning - Builds a permanent library of skills and behaviors that an agent can store and reuse over long periods.
• Automated Code Refinement Loops - Iteratively modifies generated scripts based on environment error messages to debug and improve program execution.
• Error-Correction Feedback Loops - Uses recursive feedback loops to provide structural error messages for iterative correction of generated code.
• Multi-step Goal Execution - Independently plans and carries out sequences of actions to achieve complex objectives by composing learned skills.
• Natural Language Code Generators - Translates high-level natural language instructions into executable scripts to control an agent.
• Action Controllers - Functions as a code-based action controller that translates natural language instructions into executable scripts for environment interaction.
• Automatic Curriculum Generation - Generates a sequence of exploration goals to maximize the discovery of new items and environments without human intervention.
• Agentic Goal Decomposition - Utilizes LLMs to recursively break high-level objectives into actionable sub-goals and executable skills.
• Code Refinement Loops - Refines generated programs by feeding environment feedback and error messages back into an automated refinement loop.
• Agent State Persistence - Saves the agent's progress and learned skill library to disk to allow resuming lifelong learning processes.
• Machine Learning State Management - Persists the agent's learning state and skill library to checkpoints to facilitate resumption of exploration.
• Autonomous Environment Exploration - Navigates and interacts with virtual environments autonomously to acquire new skills through exploration.
• Agent Action Frameworks - Open-ended embodied agent using language models.
• Agent Environments - Open-ended embodied agent framework for Minecraft-based tasks.
• Agent Frameworks - Embodied agent framework for complex environments.
• Agent Reflection - Open-ended embodied agent that learns and reflects through exploration.
• Autonomous Agent Frameworks - Lifelong learning agent that explores and acquires skills in Minecraft.
• Embodied Agents - Open-ended embodied agent capable of autonomous exploration.
• Embodied Agents and Tools - Agent framework for open-ended learning in simulated worlds.
• Manipulation and Control - Open-ended embodied agent for Minecraft tasks.
• Agentic AI - Listed in the “Agentic AI” section of the The Incredible Pytorch awesome list.

Star history